The　thermal　infrared　data　of　the　Landsat　series　satellite　are　the　main　data　for　the　monitoring　of　the　Earth＇s　surface　temperature　change.　The　Thermal　Infrared　Sensor　(TIRS)　of　Landsat　8,　a　new　generation　of　Landsat　series　satellites,　contributes　to　this　important　mission.　The　TIRS　data　from　Landsat　8　have　been　widely　utilized　since　its　launch　on　February　11,　2013.　Several　algorithms　for　the　derivation　of　land　surface　temperature　(LST)　from　TIRS　data　have　also　been　developed　successively.　Studies　on　TIRS-data　calibration　accuracy　and　the　precision　of　related　LST　inversion　algorithms　have　been　conducted.　This　paper　presents　a　brief　summary　of　the　changes　in　TIRS-data　calibration　parameters　and　analyzes　the　suitability　and　effectiveness　of　current　LST　inversion　algorithms　based　on　the　time　point　between　current　calibration　parameters　and　LST　inversion　algorithms.　In　particular,　the　paper　focuses　on　the　suitability　of　two　recently　proposed　split-window　algorithms　for　current　TIRS　data.　Users　and　researchers　can　therefore　acquire　further　understanding　of　the　algorithms　and　properly　apply　them　to　Landsat　8　TIRS　data.　In　general,　owing　to　the　influence　of　significant　stray　light　coming　from　outside　TIRS＇　field　of　view,　the　calibration　accuracy　of　TIRS　data　still　cannot　meet　design　goals,　and　the　uncertainty　of　TIRS　band　11　is　twice　as　large　as　that　of　TIRS　band　10　at　this　stage.　Moreover,　the　root　mean　square　error　of　both　TIRS　band　10　and　band　11　is　much　higher　than　that　of　Landsat　7　Enhanced　Thematic　Mapper　Plus　(ETM+)　band　6.Therefore,　before　the　problem　of　out-of-field　stray　light　can　be　completely　resolved,　employing　a　split-window　algorithm　to　retrieve　LST　using　both　TIRS　band　10　and　band　11　should　be　avoided　as　this　may　cause　a　large　amount　of　uncertainty　in　the　results.　Users　should　work　with　TIRS　band　10　data　as　a　single　spectral　band,　similar　to　Landsat　5　Thematic　Mapper　(TM)　band　6　or　Landsat　7　ETM+　band　6,　given　the　large　amount　of　uncertainty　in　the　band　11　values.　Users　may　use　the　single-channel　(SC)　algorithm　of　Jiménez-Muñoz　and　Sobrino　to　retrieve　LST　using　TIRS　band　10　when　the　atmospheric　water　vapor　content　is　less　than　3　g/cm2.　©　2016,　Science　Press.　All　right　reserved.